An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease

Neuroimage. 2012 Apr 15;60(3):1880-9. doi: 10.1016/j.neuroimage.2012.01.062. Epub 2012 Jan 16.


Understanding the progression of neurological diseases is vital for accurate and early diagnosis and treatment planning. We introduce a new characterization of disease progression, which describes the disease as a series of events, each comprising a significant change in patient state. We provide novel algorithms to learn the event ordering from heterogeneous measurements over a whole patient cohort and demonstrate using combined imaging and clinical data from familial Alzheimer's and Huntington's disease cohorts. Results provide new detail in the progression pattern of these diseases, while confirming known features, and give unique insight into the variability of progression over the cohort. The key advantage of the new model and algorithms over previous progression models is that they do not require a priori division of the patients into clinical stages. The model and its formulation extend naturally to a wide range of other diseases and developmental processes and accommodate cross-sectional and longitudinal input data.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Alzheimer Disease / diagnosis*
  • Alzheimer Disease / genetics*
  • Brain / pathology*
  • Computer Simulation
  • Disease Progression
  • Genetic Predisposition to Disease / genetics
  • Humans
  • Huntington Disease / diagnosis*
  • Huntington Disease / genetics*
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods
  • Magnetic Resonance Imaging / methods*
  • Models, Biological*
  • Pattern Recognition, Automated / methods
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Subtraction Technique